Various welding techniques are commonly utilized to join metallic parts to produce a wide variety of articles of manufacture such as, for example, automobile components, aircraft components, heavy equipment and machinery. The quality of the weld may play an important role in the structural integrity of the welded structure in which it is employed. However, during the welding or joining operation, defects may be introduced or formed in the weld. Such defects may include blowholes, voids, porosity and insufficient weld penetration depth. Each of these defects may decrease the load bearing capacity of the welded structure. For example, some types of defects may act as stress risers or stress concentrators which may impact the static, dynamic and fatigue strength of the weld and the welded structure. Therefore, it is important to accurately detect and locate potential defects in the welds.
When welds are formed automatically, such as by an automated or robotic welding system, the quality of a weld may be assessed by destructively testing a random sampling of the welded structures that are produced. Destructive tests, such as cut-checks, may be time-consuming and may generate excess product waste. Moreover, automation of such destructive testing methodologies may not be possible.
Efforts have been made to develop various non-destructive testing techniques for detecting defects in welds. However, most of these techniques may not be easily incorporated into manufacturing environments. Moreover, such non-destructive techniques may be unable to identify the specific types of defects present in the weld and characterize the severity of the defects.
Accordingly, a need exists for alternative methods and systems for detecting defects in welds and determining the type and severity of the detected defects.